Search NASA⌕ Search

SEARCH · Search NASA

Results for “ATOM CONCENTRATION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Solvation Structure of 237 Np 4+ in a Noncomplexing Environment

Here, the solvation structure of an Np 4+ ion in an aqueous, noncomplexing and nonoxidizing environment of trifluoromethanesulfonic (triflic) acid was investigated with X-ray absorption spectroscopy (XAS) combined with ab initio molecular dynamics (AIMD) and time-dependent density functional theory (TDDFT) calculations. Np L III -edge X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) data were collected for Np 4+ in 1, 3, and 7 M triflic acid using a laboratory-scale spectrometer and separately at a synchrotron facility, producing data sets in excellent agreement. TDDFT calculations revealed a weak pre-edge feature not previously reported for Np L III -edge XANES. AIMD modeling results showed differences in the hydration shell of the Np 4+ ion at different concentrations of triflic acid; these results are supported by the experiment. EXAFS fit models to the experiment resulted in similar coordination of Np 4+ in noncomplexing aqueous media as reported in the literature for 1 M perchloric acid but, together with calculations, revealed more than one distance between Np and O atoms in 7 M triflic acid. These results imply monodentate coordination with sulfonate groups in 7 M triflic acid and suggest the possibility of proto-neptunyl species in relatively low-concentration Np 4+ acid solutions.

Boglaienko, Daria V. [Pacific Northwest National L↗
DOE OSTI2024

An improved semi-global intrinsic kinetics model for high temperature carbon oxidation

Measurements of the oxidation rates of various forms of carbon (soot, graphite, coal char) have often shown an unexplained attenuation with increasing temperatures in the vicinity of 2000 K, even when accounting for diffusional transport limitations and gas-phase chemical effects (e.g. CO 2 dissociation). With the development of oxy-fuel combustion approaches for pulverized coal utilization with carbon capture, high particle temperatures are readily achieved in sufficiently oxygen-enriched environments. Here, in this work, a new semi-global intrinsic kinetics model for high temperature carbon oxidation is created by starting with a previously developed 5-step mechanism that was shown to reproduce all major known trends in carbon oxidation, except for its high temperature kinetic falloff, and incorporating a recently discovered surface oxide decomposition step. The predictions of this new model are benchmarked by deploying the kinetic model in a steady-state reacting particle code (SKIPPY) and comparing the simulated results against a carefully measured set of pulverized coal char combustion temperature measurements over a wide range of oxygen concentrations in N 2 and CO 2 environments. The results show that the inclusion of the spontaneous surface oxide decomposition reaction step significantly improves predictions at high particle temperatures. Furthermore, the simulations reveal that O atoms released from the oxide decomposition step enhance the radical pool in the near-surface region and within the particle interior itself. Incorporation of literature rates for O and OH reactions with the carbon surface results in a reduction in the predicted radical pool concentrations and a very minor enhancement of the overall carbon oxidation rate.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2025

Morphological Characterization of Uranyl Fluoride Particles via Atomic Force Microscopy

Uranium hexafluoride (UF 6 ) undergoes a rapid hydrolysis reaction when exposed to atmospheric water. In addition to producing hazardous HF gas, the hydrolysis reaction produces uranyl fluoride (UO 2 F 2 ), a radioactive solid phase particulate material. Because of the technological utility of UF 6 in the nuclear fuel cycle, understanding the transport properties of UO 2 F 2 aerosol produced via UF 6 hydrolysis is important for accident scenarios. Moreover, the fundamental chemical and physical properties of the UF 6 hydrolysis reaction are not completely understood. Recently, several experiments on the aerosol phase properties of UO 2 F 2 produced in this way have shown that under most relevant conditions, the particle size distribution (PSD) of UO 2 F 2 can be extremely small, approximately 3 to 5 nm, which is well below the threshold that can be routinely observed via scanning electron microscopy (SEM). Although readily observable in the aerosol phase, observation of nanometer-sized particles in the condensed phase (i.e. deposited on surfaces) remains a challenge. Here, in this study, we have used atomic force microscopy (AFM) to study the PSD and morphological characteristics of UO 2 F 2 deposited at low and high concentrations under different humidity conditions, a primary variable in the hydrolysis reaction. Here, we find strong agreement between PSD measured in the aerosol phase via scanning mobility particle sizing and PSD measured via AFM, with particle sizes peaked below 4 nm for low-humidity conditions. At higher humidity, the distribution is centered around 5 to 10 nm but extends up to 20 nm. These results are in stark contrast to previous measurements using SEM that show PSD on the order of 300- to 1000-nm particle sizes; moreover, these are the first direct measurements of individual particles of UO 2 F 2 having been produced via UF 6 hydrolysis deposited on surfaces. These measurements, therefore, open a new avenue for collecting and detecting UO 2 F 2 in the condensed phase and further refine the PSD, which is critical for environmental transport determinations.

Uranium hexafluoride↗
DOE OSTI2024

Thermodynamic and Kinetic Modulation of Methylammonium Lead Bromide Crystallization Revealed by In Situ Monitoring

Hybrid organic–inorganic perovskite (HOIP) crystals are promising optoelectronic materials, but little is known about either the thermodynamic and kinetic controls on crystal growth or the underlying growth mechanism(s). Herein, we use fluid cell atomic force microscopy (AFM) and solution nuclear magnetic resonance (NMR) spectroscopy to investigate growth of the model HOIP crystal CH 3 NH 3 PbBr 3 (MAPbBr 3 ) and to determine how formic acid (HCOOH) modulates the thermodynamics and kinetics of growth. The results show that growth of MAPbBr 3 in dimethylformamide (DMF) proceeds through the classical pathway by the spreading of atomic crystal steps generated at screw dislocations on the {100} surface. Temperature dependent step velocity measurements demonstrate that with increasing concentration, HCOOH decreases both the solubility of MAPbBr 3 and the kinetic coefficient (b) of step movement. 1 H-NMR measurements indicate that HCOOH increases the lifetime of the methylammonium (MA + ) ions and promotes the association of MAPbBr 3 , thus tuning the solubility of the perovskite. HCOOH also alters the molecular tumbling motion and bulk diffusion of the MA + ions, possibly via H-bonding. Further, our findings establish a direct correlation between the mesoscale crystal growth kinetics and the molecular-scale interactions between organic additives and constituent ions, providing unprecedented insights for developing predictive syntheses of HOIP crystals with defined size, crystal habit and shape, and defect distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hydrogen and deuterium tunneling in niobium

We use density functional methods to identify the atomic configurations of H and D atoms trapped by O impurities embedded in bulk Nb. The O atoms are located at the octahedral position in the Nb body-centered cubic (BCC) lattice, and H (D) atoms tunnel between two degenerate tetrahedral sites separated by a mirror plane. Using nudged elastic band (NEB) methods, we calculate the double-well potential for O-H and O-D impurities and the wave functions and tunnel splittings for H and D atoms. Our results agree with those obtained from analysis of heat capacity and neutron scattering measurements on Nb with low concentrations of O-H and O-D.

density functional theory↗
DOE OSTI2025

Demonstrate new plasticity models for doped UO 2 that capture dislocation mechanisms

In light water reactors, fuel vendors are investigating the use of dopants to modify the properties of UO 2 pellets, with the goal of improving pellet-cladding mechanical interactions during operation. Dopants are expected to ‘soften’ the pellets; that is, the doped pellets have higher plastic deformation than conventional UO 2 . This leads to a reduction in the severity of mechanical pellet-cladding interactions, helping to reduce the hoop strain on the cladding. By minimizing the strain exerted by the pellet on the cladding, it is anticipated that cladding performance under accident conditions can be enhanced (i.e., lowering the risk of burst during a LOCA). Dopants such as chromium (Cr) promote grain growth during pellet fabrication, leading to larger grains; therefore, understanding the link between chemistry, microstructure and mechanical deformation (enhanced creep rates) behavior of UO 2 is critical to helping operators further substantiate the benefits of doping UO 2 . Historically, the nuclear energy industry has relied on empirical models to make assessments of performance. Compared to empirical models, mechanistic physics-based models provide benefits, such as, fewer data points for validation and better extrapolation where experimental data is scarce or non-existent. In this report, Bayesian inference techniques have been applied to a previously developed lower length-scale-informed diffusional creep model. The objective is to i) infer lower-length-scale parameter distributions from available experiment and then ii) determine the uncertainties in the measurable quantity (in this case creep rates) after propagating the inferred lower length scale parameter uncertainties. The approach requires many evaluations of the model, which becomes computationally insurmountable; therefore, a neural-network model is trained to data obtained by sampling the full model over the most important parameters. This neural-network is then used in the Bayesian inference approach to determine probability distributions in the parameter values that represent the uncertainty in the model given what is known from the experiments (posterior). A significant reduction compared to conservative initial (prior) uncertainties is achieved through inference against the experimental data, demonstrating the efficacy of this approach. Furthermore, by accounting for uncertainties in the experimental conditions and sample non-stoichiometry, it is possible to resolve apparent discrepancies in experimental measurements within a self-consistent grain boundary (Coble) creep model that is sensitive to chemistry. This work has been written up and submitted to Nuclear Technology for a special issue on accelerated fuel qualification (AFQ). This uncertainty quantification (UQ) work not only improves the diffusional model, while accounting for uncertainty, but also establishes a framework which can readily be applied to the mechanistic models of dislocation deformation developed in this study. The most likely values from the Bayesian analysis are incorporated into our UO 2 diffusional creep model and a lower length scale-informed irradiation UO 2 creep mechanistic model to generate a dataset. This dataset has been provided to our INL collaborators for training an artificial neural network surrogate model, which will be implemented in the BISON fuel performance code to assess how the results differ from those currently obtained using a fully empirical model and that of using the nominal (uncalibrated) atomic scale parameters in our mechanistic model. Plastic deformation (creep and glide) in UO 2 is a complex phenomenon, governed by multiple underlying processes such as local defect concentrations, applied stresses, and microstructural characteristics. Consequently, there is a need for a meso-scale model with polycrystalline resolution capable of extrapolating to large grain sizes applicable to doped UO 2 , where data is limited and the model can help bridge the knowledge gap. By integrating atomistic data into the polycrystal LApx code, it becomes possible to predict dislocation climb and glide plasticity that simple analytical models cannot accurately represent. The application of atomic-scale data within LApx demonstrated the importance of climb and glide mechanisms in reproducing high-stress UO 2 behavior. Behaviors such as this are crucial to capture and implement in BISON, as parts of the fuel pellet can reach temperatures where glide can occur before pellet cracking. This model which captures dislocation based mechanisms for UO 2 is then used to stand up the doped model accounting for larger grain sizes. It was found that larger grain sizes can lead to enhanced deformation rates in the glide regime, and therefore can help with the pellet cladding mechanical interaction. Therefore if the fuel pellet reaches conditions (stress/temperature) where glide is active, the enhanced creep rates for larger grains in the glide regime (doped UO 2 ) can help with pellet cladding mechanical interactions. Plastic deformation in UO 2 involves multiple mechanisms, including diffusional creep, dislocation climb, and glide. This milestone contains two parts: (1) UQ of a pre-existing lower length scale informed mechanistic diffusional creep model, and (2) development of a new LApx based model for dislocation-mediated creep mechanisms in UO 2 , with application to large-grain doped UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
DOE OSTI2024

Stratification of fluoride uptake among enamel crystals with age elucidated by atom probe tomography

Dental enamel is subjected to a lifetime of de- and re-mineralization cycles in the oral environment, the cumulative effects of which cause embrittlement with age. However, the understanding of atomic scale mechanisms of dental enamel aging is still at its infancy, particularly regarding where compositional differences occur in the hydroxyapatite nanocrystals and what underlying mechanisms might be responsible. Here, we use atom probe tomography to compare enamel from a young (22 years old) and a senior (56 years old) adult donor tooth. Findings reveal that the concentration of fluorine is elevated in the shells of senior nanocrystals relative to young, with less significant differences between the cores or intergranular phases. It is proposed that the embrittlement of enamel is driven, at least in part, by the infusion of fluorine into the nanocrystals and that the principal mechanism is de- and re-mineralization cycles that preferentially erode and rebuild the nanocrystals shells.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Achieving Electrode Smoothing by Controlling the Nucleation Phase of Metal Deposition Through Polymer‐Substrate Binding

Polymer additives [like polyethylene oxide (PEO)] are widely used for smooth electrode deposition in aqueous zinc and many other battery systems. However, the precise mechanism by which they regulate morphology and suppress dendrite formation remains unclear. In this study, the knowledge gap is addressed by using in situ electrochemical atomic force microscopy to directly observe the interfacial evolution during Zn electrodeposition and polymer adsorption on Cu substrates in the presence of varying concentrations of ZnSO 4 and PEO. Contrary to previous literature assumptions, which emphasize the binding to the growing Zn crystal surfaces or Zn 2+ ions, the results demonstrate that PEO smooths Zn films by promoting nucleation of (002)-oriented Zn platelets through interactions with the Cu substrate. Density functional theory simulations support this finding by showing that PEO adsorption on Cu modifies the interfacial energy of Zn/Cu/electrolyte interfaces, favoring the stabilization of Zn (002) on the Cu substrate, as well as confines Zn electrodeposition to a narrow near-surface region. In conclusion, these findings elucidate a novel design principle for electrode smoothing, emphasizing the importance of substrate selection paired with polymer additives that exhibit an attractive interaction with the substrate but minimal interaction with growing crystals, offering a mechanistic perspective for improved battery performance.

Electrodeposition↗
DOE OSTI2024

Trace benzene capture by decoration of structural defects in metal–organic framework materials

Abstract Capture of trace benzene is an important and challenging task. Metal–organic framework materials are promising sorbents for a variety of gases, but their limited capacity towards benzene at low concentration remains unresolved. Here we report the adsorption of trace benzene by decorating a structural defect in MIL-125-defect with single-atom metal centres to afford MIL-125-X (X = Mn, Fe, Co, Ni, Cu, Zn; MIL-125, Ti 8 O 8 (OH) 4 (BDC) 6 where H 2 BDC is 1,4-benzenedicarboxylic acid). At 298 K, MIL-125-Zn exhibits a benzene uptake of 7.63 mmol g −1 at 1.2 mbar and 5.33 mmol g −1 at 0.12 mbar, and breakthrough experiments confirm the removal of trace benzene (from 5 to <0.5 ppm) from air (up to 111,000 min g −1 of metal–organic framework), even after exposure to moisture. The binding of benzene to the defect and open Zn(II) sites at low pressure has been visualized by diffraction, scattering and spectroscopy. This work highlights the importance of fine-tuning pore chemistry for designing adsorbents for the removal of air pollutants.

Chemistry↗
DOE OSTI2025

The effect of composition on helium bubble nucleation in complex concentrated alloys

Molecular dynamics simulations of helium implantation have been performed in the MoNbTaTi complex concentrated alloy (CCA) to determine the impact of composition on helium clustering and bubble nucleation. It was found that compositions with lower interstitial atomic volume resulted in smaller cluster sizes, higher helium-to-vacancy ratios, and larger migration barriers. As the atomic volume decreases, it is harder to accommodate additional helium in the lattice and for helium to diffuse between interstitial sites, resulting in smaller cluster sizes. In particular, increasing molybdenum directly correlated with a decreased interstitial atomic volume and increased migration barrier. Niobium exhibited the opposite trend, where more niobium in the material resulted in larger interstitial atomic volumes and cluster sizes but lower migration barriers. The atomic volume was determined to be an indicator of the susceptibility of the material to helium bubble growth and could potentially be used as a metric to screen compositions for resistance to helium damage. In this way, properties such as available interstitial volumes could be used to screen materials more rapidly given the vast compositional space of CCAs.

McCarthy, Megan Jeanne [Sandia National Laboratori↗
DOE OSTI2025

Molecular understanding of ion transport in a zwitterionic electrolyte

Zwitterions (ZIs) are unique molecules that carry both positive and negative charges, resulting in overall charge neutrality and high dielectric constants. These distinctive properties have enabled broad applications of zwitterionic functionality, including the emerging use of ZIs in lithium-ion battery electrolytes. As a contribution to this developing field, we use all-atom molecular dynamics simulations to investigate the ion transport mechanisms in amorphous mixtures of a zwitterionic liquid containing a range of LiTFSI salt concentrations. Furthermore, the local coordination environment around the Li + ions plays a strong role in governing ionic conductivity, as well as the enhancement of Li + transport numbers with increasing salt concentration. Addition of small amounts of water leads to increased conductivity and ion mobilities due to the water coordinating with the Li + ions, which reduces direct interactions with larger charged species.

Classical molecular dynamic simulations↗
DOE OSTI2025

Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential

Zn Cl 2 solutions are promising electrolytes for aqueous zinc-ion batteries. Here, we report a joint computational and experimental study of the structural and dynamic properties of aqueous Zn Cl 2 electrolytes with concentrations ranging from salt-in-water to water-in-salt (WIS). By developing a neural network potential (NNP) model, we perform molecular dynamics (MD) simulations with accuracy but at much larger lengths and longer timescales. The NNP predicted structures are validated by the structure factors measured by X-ray total scattering experiments. The MD trajectories provide a comprehensive and quantitative picture of the Zn 2 + solvation shell structures. Additionally, we find that the O − H covalent bonds in water are strengthened with increasing salt concentration, thus expanding the electrochemical stability window of aqueous electrolytes. In terms of dynamic properties, the calculated and experimentally measured conductivities are in good agreement. Through the analysis of the calculated cation transference number, we propose a three-stage charge carrier transport mechanism with increasing concentration: independent ion transport, strongly correlated ion transport, and small positive charge carrier diffusion through negatively charged polymeric clusters. Our study provides fundamental atomic scale insights into the structure and transport properties of the Zn Cl 2 electrolyte that can aid the optimization and development of WIS electrolytes. Published by the American Physical Society 2025

25 ENERGY STORAGE↗
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2025

Effects of break geometry and orientation on helium-air mixing in simulated reactor cavities of high temperature gas reactors

Here, this study experimentally examined the spatial and temporal variations in air and helium concentrations and temperature fields within simulated reactor cavities of a High Temperature Gas Reactor (HTGR) following helium discharge into an initially air-filled reactor cavity system. Detailed temperature maps were generated using a combination of fiber optics temperature sensor and multiple thermocouple probes within the simulated reactor cavities. The research scenario involved a hypothetical small pipe break in the Reactor Pressure Vessel, resulting in the release of high-temperature helium into the surrounding cavity. A scaled multi-compartment experimental facility, modeled after the General Atomics Modular High Temperature Gas Reactor (GA-MHTGR) design, was constructed for helium and air mixing experiments. Oxygen sensors and thermocouple probes were installed in all five cavities to measure the concentrations of oxygen (or helium) and the temperature distributions of the gas mixture. The experimental findings highlighted the significant impact of the injected helium jet velocity on the gas mixing process and demonstrated how the direction of the helium jet influences the air-helium temperature profiles within the cavities.

Air-ingress↗
DOE OSTI2025

Pseudo-equilibrium theory for extrinsic doping control of the topological semimetal Cd 3 As 2

The standard approach for predicting defect equilibria from first principles assumes that the solid-state system is initially in a thermodynamic equilibrium with the external atomic reservoirs. This “growth step” is then often followed by a temperature quench in a “pseudo-equilibrium” in which some or all defect concentrations are frozen in until only the Fermi level E F remains to be equilibrated. However, this protocol does not account for the possibility of site exchanges which can create important defect redistributions as long as short-range defect migration is kinetically permissible. To model this redistribution, we developed an approach to solve for the non-equilibrium chemical potentials as a function of temperature while maintaining the overall defect stoichiometry. We then apply this approach to the Dirac semimetal Cd 3 As 2 to model extrinsic doping with group 1/11 and 14 elements. Undoped Cd 3 As 2 exhibits an undesirable mismatch between E F and the Dirac point. This unintentional electron doping originates from intrinsic defects and is difficult to overcome through adjustment of synthesis conditions alone. Employing our pseudo-equilibrium modeling, we identify extrinsic doping strategies for realizing doping-balanced Cd 3 As 2 at the relatively low temperatures accessible in thin-film growth of this material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unraveling the transformation pathway of the 𝛽 to 𝛾 phase transition in Ga 2 ⁢O 3 from atomistic simulations

Defect spinel 𝛾−Ga 2 ⁢O 3 is the least stable polymorph of Ga 2 ⁢O 3 , so its frequent appearance as a structural defect within or on the surface of monoclinic 𝛽−Ga 2 ⁢O 3 remains a mystery. Through first-principles calculations, we explore potential pathways for the phase transition from 𝛽−Ga 2⁢ O 3 to 𝛾−Ga 2 ⁢O 3 , and examine two key driving forces: tensile strain and Ga deficiency. When configurational entropy contributions to phase energies are included, the 𝛾 phase becomes energetically competitive with the 𝛽 phase, with the free energy difference between these phases diminishing even further under Ga-deficient conditions. Notably, a stability crossover occurs at room temperature at high vacancy concentrations ([V$^{3−}_{Ga}$]>3%) . A simple model 𝛽 → 𝛾 transformation pathway is identified, comprising two primary reactions, that enables the formation of the 𝛾 phase via simultaneous migration of Ga atoms from tetrahedral lattice sites to octahedral interstitial positions. The transformation barriers are prohibitively large in pristine Ga 2 ⁢O 3 , but can be substantially reduced by: (1) the presence of Ga vacancies, (2) elongational strains along the crystallographic 𝑎-axis, and (3) when volumetric relaxations are possible during transformation. These results elucidate prior experimental observations, where 𝛾−Ga 2⁢ O 3 is seen on damaged surfaces or in highly 𝑛-type 𝛽−Ga 2⁢ O 3 environments, which support Ga deficiency and mechanical strain. The insights into the driving forces and mechanisms of 𝛾−Ga 2⁢ O 3 formation enhance understanding of how localized strain and nonequilibrium defect concentrations may facilitate its formation from the 𝛽 phase.

Defects↗